{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pp \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>station</td>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Present_Tmax</td>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Present_Tmin</td>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_RHmin</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_RHmax</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_Tmax_lapse</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_Tmin_lapse</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_WS</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_LH</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC1</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC2</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC3</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC4</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_PPT1</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_PPT2</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_PPT3</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_PPT4</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lat</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lon</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DEM</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slope</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solar radiation</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Next_Tmax</td>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Next_Tmin</td>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      "station             7750 non-null float64\n",
      "Date                7750 non-null object\n",
      "Present_Tmax        7682 non-null float64\n",
      "Present_Tmin        7682 non-null float64\n",
      "LDAPS_RHmin         7677 non-null float64\n",
      "LDAPS_RHmax         7677 non-null float64\n",
      "LDAPS_Tmax_lapse    7677 non-null float64\n",
      "LDAPS_Tmin_lapse    7677 non-null float64\n",
      "LDAPS_WS            7677 non-null float64\n",
      "LDAPS_LH            7677 non-null float64\n",
      "LDAPS_CC1           7677 non-null float64\n",
      "LDAPS_CC2           7677 non-null float64\n",
      "LDAPS_CC3           7677 non-null float64\n",
      "LDAPS_CC4           7677 non-null float64\n",
      "LDAPS_PPT1          7677 non-null float64\n",
      "LDAPS_PPT2          7677 non-null float64\n",
      "LDAPS_PPT3          7677 non-null float64\n",
      "LDAPS_PPT4          7677 non-null float64\n",
      "lat                 7752 non-null float64\n",
      "lon                 7752 non-null float64\n",
      "DEM                 7752 non-null float64\n",
      "Slope               7752 non-null float64\n",
      "Solar radiation     7752 non-null float64\n",
      "Next_Tmax           7725 non-null float64\n",
      "Next_Tmin           7725 non-null float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
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       "      <td>22.907420</td>\n",
       "      <td>11.017837</td>\n",
       "      <td>44.002020</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5925.883789</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <td>271</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7/10/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.196007</td>\n",
       "      <td>95.168205</td>\n",
       "      <td>28.097980</td>\n",
       "      <td>24.510159</td>\n",
       "      <td>8.374849</td>\n",
       "      <td>38.782242</td>\n",
       "      <td>...</td>\n",
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       "      <td>21.9668</td>\n",
       "      <td>0.133200</td>\n",
       "      <td>5772.487305</td>\n",
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       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7/12/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.027298</td>\n",
       "      <td>99.209839</td>\n",
       "      <td>24.078120</td>\n",
       "      <td>21.866817</td>\n",
       "      <td>8.543768</td>\n",
       "      <td>9.371270</td>\n",
       "      <td>...</td>\n",
       "      <td>5.055660</td>\n",
       "      <td>1.347418</td>\n",
       "      <td>0.980052</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5893.265625</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.5</td>\n",
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       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7/18/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.891193</td>\n",
       "      <td>94.747780</td>\n",
       "      <td>29.195536</td>\n",
       "      <td>23.236973</td>\n",
       "      <td>10.881031</td>\n",
       "      <td>79.349271</td>\n",
       "      <td>...</td>\n",
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       "      <td>37.6046</td>\n",
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       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5812.293457</td>\n",
       "      <td>27.6</td>\n",
       "      <td>21.8</td>\n",
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       "    <tr>\n",
       "      <td>464</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7/18/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.795406</td>\n",
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       "      <td>25.607262</td>\n",
       "      <td>8.995135</td>\n",
       "      <td>26.022306</td>\n",
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       "      <td>30.0464</td>\n",
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       "      <td>5681.875000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>35.0380</td>\n",
       "      <td>0.505500</td>\n",
       "      <td>4602.118164</td>\n",
       "      <td>26.1</td>\n",
       "      <td>17.9</td>\n",
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       "    <tr>\n",
       "      <td>7682</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8/28/2017</td>\n",
       "      <td>26.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>29.959215</td>\n",
       "      <td>90.116638</td>\n",
       "      <td>23.135079</td>\n",
       "      <td>17.282587</td>\n",
       "      <td>9.292264</td>\n",
       "      <td>75.430868</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4518.488770</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>7707</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4478.937012</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4562</td>\n",
       "      <td>126.826</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>17.4</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>98.524734</td>\n",
       "      <td>100.000153</td>\n",
       "      <td>38.542255</td>\n",
       "      <td>29.619342</td>\n",
       "      <td>21.857621</td>\n",
       "      <td>213.414006</td>\n",
       "      <td>...</td>\n",
       "      <td>21.621661</td>\n",
       "      <td>15.841235</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.135</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.9</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  \\\n",
       "225       1.0   7/9/2013           NaN           NaN    70.051193   \n",
       "271      22.0  7/10/2013           NaN           NaN    72.196007   \n",
       "300       1.0  7/12/2013           NaN           NaN    95.027298   \n",
       "450       1.0  7/18/2013           NaN           NaN    60.891193   \n",
       "464      15.0  7/18/2013           NaN           NaN    52.795406   \n",
       "...       ...        ...           ...           ...          ...   \n",
       "7629      5.0  8/26/2017           NaN           NaN    43.755058   \n",
       "7682      8.0  8/28/2017          26.3          18.1    29.959215   \n",
       "7707      8.0  8/29/2017           NaN           NaN    44.392651   \n",
       "7750      NaN        NaN          20.0          11.3    19.794666   \n",
       "7751      NaN        NaN          37.6          29.9    98.524734   \n",
       "\n",
       "      LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_Tmin_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "225     99.668961         27.872808         22.907420  11.017837   44.002020   \n",
       "271     95.168205         28.097980         24.510159   8.374849   38.782242   \n",
       "300     99.209839         24.078120         21.866817   8.543768    9.371270   \n",
       "450     94.747780         29.195536         23.236973  10.881031   79.349271   \n",
       "464     83.902847         31.480089         25.607262   8.995135   26.022306   \n",
       "...           ...               ...               ...        ...         ...   \n",
       "7629    83.340240         25.842338         18.532986   4.926595   97.230757   \n",
       "7682    90.116638         23.135079         17.282587   9.292264   75.430868   \n",
       "7707    75.728195         22.223247         15.954970   4.764492   37.786237   \n",
       "7750    58.936283         17.624954         14.272646   2.882580  -13.603212   \n",
       "7751   100.000153         38.542255         29.619342  21.857621  213.414006   \n",
       "\n",
       "      ...  LDAPS_PPT2  LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM  \\\n",
       "225   ...    0.036680    0.000000    0.000000  37.6046  126.991  212.3350   \n",
       "271   ...    0.007261    0.000000    0.000000  37.5102  127.086   21.9668   \n",
       "300   ...    5.055660    1.347418    0.980052  37.6046  126.991  212.3350   \n",
       "450   ...    0.000000    0.000000    0.057358  37.6046  126.991  212.3350   \n",
       "464   ...    0.000000    0.000000    0.008702  37.5507  126.937   30.0464   \n",
       "...   ...         ...         ...         ...      ...      ...       ...   \n",
       "7629  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7682  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7707  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7750  ...    0.000000    0.000000    0.000000  37.4562  126.826   12.3700   \n",
       "7751  ...   21.621661   15.841235   16.655469  37.6450  127.135  212.3350   \n",
       "\n",
       "         Slope  Solar radiation  Next_Tmax  Next_Tmin  \n",
       "225   2.785000      5925.883789       23.4       22.0  \n",
       "271   0.133200      5772.487305       26.1       24.1  \n",
       "300   2.785000      5893.265625       23.2       20.5  \n",
       "450   2.785000      5812.293457       27.6       21.8  \n",
       "464   0.855200      5681.875000       30.7       23.4  \n",
       "...        ...              ...        ...        ...  \n",
       "7629  0.505500      4602.118164       26.1       17.9  \n",
       "7682  1.562900      4518.488770        NaN        NaN  \n",
       "7707  1.562900      4478.937012       23.1       16.3  \n",
       "7750  0.098475      4329.520508       17.4       11.3  \n",
       "7751  5.178230      5992.895996       38.9       29.8  \n",
       "\n",
       "[164 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "df1 = Data[Data.isnull().T.any()]\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='station', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Tmax', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Tmin', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_RHmin', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_RHmax', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_Tmax_lapse', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_Tmin_lapse', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_WS', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_LH', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC1', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC2', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC3', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC4', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT1', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT2', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT3', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT4', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lat', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lon', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEM', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Slope', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Solar radiation', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmax', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmin', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = Data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "    sns.distplot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成数据概况报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a05198e0a49f44b28eb9dc271445f3ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/38 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/visualisation/context.py\u001b[0m in \u001b[0;36mmanage_matplotlib_context\u001b[0;34m()\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0mregister_matplotlib_converters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m         \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustomRcParams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_misc.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(explicit)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mderegister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_get_plot_backend\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m   1600\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfigs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m             >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m from pandas.plotting._matplotlib.boxplot import (\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mBoxPlot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAbstractMethodError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    434\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_repr_html_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0;34m\"\"\"The ipython notebook widgets user interface gets called by the jupyter notebook.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_notebook_iframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_notebook_iframe\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_notebook_iframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_widgets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/report/presentation/flavours/widget/notebook.py\u001b[0m in \u001b[0;36mget_notebook_iframe\u001b[0;34m(profile)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_notebook_iframe_src\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"srcdoc\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_notebook_iframe_srcdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/report/presentation/flavours/widget/notebook.py\u001b[0m in \u001b[0;36mget_notebook_iframe_srcdoc\u001b[0;34m(profile)\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"notebook\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"iframe\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"width\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"notebook\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"iframe\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"height\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0msrc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mescape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0miframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'<iframe width=\"{width}\" height=\"{height}\" srcdoc=\"{src}\" frameborder=\"0\" allowfullscreen></iframe>'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \"\"\"\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mhtml\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_render_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflavours\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTMLReport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m         \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0mdisable_progress_bar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"progress_bar\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mreport\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_report_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mdescription_set\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_description_set\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             self._description_set = describe_df(\n\u001b[0;32m--> 175\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummarizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m             )\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_description_set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/model/describe.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(title, df, summarizer, typeset, sample)\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;31m# Scatter matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_postfix_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Get scatter matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m         \u001b[0mscatter_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_scatter_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/model/summary.py\u001b[0m in \u001b[0;36mget_scatter_matrix\u001b[0;34m(df, continuous_variables)\u001b[0m\n\u001b[1;32m    289\u001b[0m                     \u001b[0mdf_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m                     scatter_matrix[x][y] = scatter_pairwise(\n\u001b[0;32m--> 291\u001b[0;31m                         \u001b[0mdf_temp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_temp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m                     )\n\u001b[1;32m    293\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recreate_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/visualisation/context.py\u001b[0m in \u001b[0;36mmanage_matplotlib_context\u001b[0;34m()\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0mderegister_matplotlib_converters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# revert to original unit registries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmplDeprecation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_misc.py\u001b[0m in \u001b[0;36mderegister\u001b[0;34m()\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0mplot_backend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_plot_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matplotlib\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mderegister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m def scatter_matrix(\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_get_plot_backend\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m   1599\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfigs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m             >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n\u001b[1;32m   1603\u001b[0m             ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTYPE_CHECKING\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mType\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m from pandas.plotting._matplotlib.boxplot import (\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mBoxPlot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mboxplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcreate_subplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflatten_axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAbstractMethodError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decorators\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a239d8d1dc6246e295223ac5a9709edb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/38 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/visualisation/context.py\u001b[0m in \u001b[0;36mmanage_matplotlib_context\u001b[0;34m()\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0mregister_matplotlib_converters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m         \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustomRcParams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_misc.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(explicit)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mderegister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_get_plot_backend\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m   1600\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfigs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m             >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m from pandas.plotting._matplotlib.boxplot import (\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mBoxPlot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAbstractMethodError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-183bb177293f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# 也可以把生成的报告导出保存\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdata_report\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'report.html'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_file\u001b[0;34m(self, output_file, silent)\u001b[0m\n\u001b[1;32m    276\u001b[0m                 \u001b[0mcreate_html_assets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0moutput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuffix\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\".html\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \"\"\"\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mhtml\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_render_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflavours\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTMLReport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m         \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0mdisable_progress_bar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"progress_bar\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mreport\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_report_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mdescription_set\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_description_set\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             self._description_set = describe_df(\n\u001b[0;32m--> 175\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummarizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m             )\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_description_set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/model/describe.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(title, df, summarizer, typeset, sample)\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;31m# Scatter matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_postfix_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Get scatter matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m         \u001b[0mscatter_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_scatter_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/model/summary.py\u001b[0m in \u001b[0;36mget_scatter_matrix\u001b[0;34m(df, continuous_variables)\u001b[0m\n\u001b[1;32m    289\u001b[0m                     \u001b[0mdf_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m                     scatter_matrix[x][y] = scatter_pairwise(\n\u001b[0;32m--> 291\u001b[0;31m                         \u001b[0mdf_temp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_temp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m                     )\n\u001b[1;32m    293\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recreate_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas_profiling/visualisation/context.py\u001b[0m in \u001b[0;36mmanage_matplotlib_context\u001b[0;34m()\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0mderegister_matplotlib_converters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# revert to original unit registries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmplDeprecation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_misc.py\u001b[0m in \u001b[0;36mderegister\u001b[0;34m()\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0mplot_backend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_plot_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matplotlib\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mderegister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m def scatter_matrix(\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_get_plot_backend\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m   1599\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfigs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m             >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n\u001b[1;32m   1603\u001b[0m             ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTYPE_CHECKING\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mType\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m from pandas.plotting._matplotlib.boxplot import (\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mBoxPlot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mboxplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcreate_subplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflatten_axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAbstractMethodError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decorators\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)"
     ]
    }
   ],
   "source": [
    "# 生成整体数据概要\n",
    "data_report=pp.ProfileReport(Data) \n",
    "data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "data_report.to_file('report.html') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      "Present_Tmax        7752 non-null float64\n",
      "LDAPS_RHmax         7752 non-null float64\n",
      "LDAPS_Tmax_lapse    7752 non-null float64\n",
      "LDAPS_WS            7752 non-null float64\n",
      "LDAPS_LH            7752 non-null float64\n",
      "LDAPS_CC1           7752 non-null float64\n",
      "LDAPS_CC2           7752 non-null float64\n",
      "LDAPS_CC3           7752 non-null float64\n",
      "LDAPS_CC4           7752 non-null float64\n",
      "LDAPS_PPT1          7752 non-null float64\n",
      "LDAPS_PPT4          7752 non-null float64\n",
      "lat                 7752 non-null float64\n",
      "lon                 7752 non-null float64\n",
      "DEM                 7752 non-null float64\n",
      "Slope               7752 non-null float64\n",
      "Solar radiation     7752 non-null float64\n",
      "Next_Tmax           7752 non-null float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Present_Tmax</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>29.709127</td>\n",
       "      <td>3.020660</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_RHmax</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>87.806292</td>\n",
       "      <td>9.182157</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>84.038809</td>\n",
       "      <td>89.699505</td>\n",
       "      <td>93.704500</td>\n",
       "      <td>100.000153</td>\n",
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       "    <tr>\n",
       "      <td>LDAPS_Tmax_lapse</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>29.554421</td>\n",
       "      <td>2.993085</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.601014</td>\n",
       "      <td>29.662273</td>\n",
       "      <td>31.683819</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_WS</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>7.633934</td>\n",
       "      <td>5.843028</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.686487</td>\n",
       "      <td>6.563068</td>\n",
       "      <td>8.092622</td>\n",
       "      <td>62.505019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_LH</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.903856</td>\n",
       "      <td>34.113647</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>36.776474</td>\n",
       "      <td>56.487289</td>\n",
       "      <td>83.904586</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC1</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>0.368651</td>\n",
       "      <td>0.261188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.148321</td>\n",
       "      <td>0.320110</td>\n",
       "      <td>0.571696</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC2</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>0.355716</td>\n",
       "      <td>0.256836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142328</td>\n",
       "      <td>0.315667</td>\n",
       "      <td>0.554963</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC3</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>0.318218</td>\n",
       "      <td>0.249155</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103100</td>\n",
       "      <td>0.265264</td>\n",
       "      <td>0.494007</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_CC4</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>0.302024</td>\n",
       "      <td>0.254732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.082741</td>\n",
       "      <td>0.232354</td>\n",
       "      <td>0.507008</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_PPT1</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>0.590959</td>\n",
       "      <td>1.936360</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067593</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LDAPS_PPT4</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>0.630043</td>\n",
       "      <td>3.841269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>37.544722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lat</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lon</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DEM</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slope</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solar radiation</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Next_Tmax</td>\n",
       "      <td>7752.0</td>\n",
       "      <td>30.249312</td>\n",
       "      <td>3.152383</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7752.0    29.709127    3.020660    20.000000    27.700000   \n",
       "LDAPS_RHmax       7752.0    87.806292    9.182157    29.613447    84.038809   \n",
       "LDAPS_Tmax_lapse  7752.0    29.554421    2.993085    17.624954    27.601014   \n",
       "LDAPS_WS          7752.0     7.633934    5.843028     2.882580     5.686487   \n",
       "LDAPS_LH          7752.0    61.903856   34.113647   -13.603212    36.776474   \n",
       "LDAPS_CC1         7752.0     0.368651    0.261188     0.000000     0.148321   \n",
       "LDAPS_CC2         7752.0     0.355716    0.256836     0.000000     0.142328   \n",
       "LDAPS_CC3         7752.0     0.318218    0.249155     0.000000     0.103100   \n",
       "LDAPS_CC4         7752.0     0.302024    0.254732     0.000000     0.082741   \n",
       "LDAPS_PPT1        7752.0     0.590959    1.936360     0.000000     0.000000   \n",
       "LDAPS_PPT4        7752.0     0.630043    3.841269     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7752.0    30.249312    3.152383    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.699505    93.704500   100.000153  \n",
       "LDAPS_Tmax_lapse    29.662273    31.683819    38.542255  \n",
       "LDAPS_WS             6.563068     8.092622    62.505019  \n",
       "LDAPS_LH            56.487289    83.904586   213.414006  \n",
       "LDAPS_CC1            0.320110     0.571696     0.967277  \n",
       "LDAPS_CC2            0.315667     0.554963     0.968353  \n",
       "LDAPS_CC3            0.265264     0.494007     0.983789  \n",
       "LDAPS_CC4            0.232354     0.507008     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.067593    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000693    37.544722  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "avg = Max.mean(axis=0)\n",
    "d = dict(zip(Max.columns,avg))\n",
    "Max = Max.fillna(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:10.245846Z",
     "start_time": "2020-06-16T01:34:10.235840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Present_Tmax</th>\n",
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       "      <th>Next_Tmax</th>\n",
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       "      <td>28.7</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5992.895996</td>\n",
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       "      <td>1</td>\n",
       "      <td>31.9</td>\n",
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       "      <td>5.691890</td>\n",
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       "      <td>0.266100</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
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       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.534800</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
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       "      <td>4</td>\n",
       "      <td>31.4</td>\n",
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       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
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       "      <td>0.151407</td>\n",
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       "      <td>0.170021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.505500</td>\n",
       "      <td>5859.552246</td>\n",
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       "      <td>7.289264</td>\n",
       "      <td>9.090034</td>\n",
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       "      <td>37.5237</td>\n",
       "      <td>126.970</td>\n",
       "      <td>19.5844</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>4451.345215</td>\n",
       "      <td>27.8</td>\n",
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       "      <td>7750</td>\n",
       "      <td>20.0</td>\n",
       "      <td>58.936283</td>\n",
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       "      <td>213.414006</td>\n",
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       "      <td>0.968353</td>\n",
       "      <td>0.983789</td>\n",
       "      <td>0.974710</td>\n",
       "      <td>23.701544</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.6450</td>\n",
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       "      <td>212.3350</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.9</td>\n",
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      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "0             28.7    91.116364         28.074101   6.818887   69.451805   \n",
       "1             31.9    90.604721         29.850689   5.691890   51.937448   \n",
       "2             31.6    83.973587         30.091292   6.138224   20.573050   \n",
       "3             32.0    96.483688         29.704629   5.650050   65.727144   \n",
       "4             31.4    90.155128         29.113934   5.735004  107.965535   \n",
       "...            ...          ...               ...        ...         ...   \n",
       "7747          23.3    78.869858         26.352081   6.148918   72.058294   \n",
       "7748          23.3    77.294975         27.010193   6.542819   47.241457   \n",
       "7749          23.2    77.243744         27.939516   7.289264    9.090034   \n",
       "7750          20.0    58.936283         17.624954   2.882580  -13.603212   \n",
       "7751          37.6   100.000153         38.542255  21.857621  213.414006   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0      0.233947   0.203896   0.161697   0.130928    0.000000    0.000000   \n",
       "1      0.225508   0.251771   0.159444   0.127727    0.000000    0.000000   \n",
       "2      0.209344   0.257469   0.204091   0.142125    0.000000    0.000000   \n",
       "3      0.216372   0.226002   0.161157   0.134249    0.000000    0.000000   \n",
       "4      0.151407   0.249995   0.178892   0.170021    0.000000    0.000000   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747   0.030034   0.081035   0.000000   0.000000    0.000000    0.000000   \n",
       "7748   0.035874   0.074962   0.000000   0.000000    0.000000    0.000000   \n",
       "7749   0.048954   0.059869   0.000000   0.000796    0.000000    0.000000   \n",
       "7750   0.000000   0.000000   0.000000   0.000000    0.000000    0.000000   \n",
       "7751   0.967277   0.968353   0.983789   0.974710   23.701544   16.655469   \n",
       "\n",
       "          lat      lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     37.6046  126.991  212.3350  2.785000      5992.895996       29.1  \n",
       "1     37.6046  127.032   44.7624  0.514100      5869.312500       30.5  \n",
       "2     37.5776  127.058   33.3068  0.266100      5863.555664       31.1  \n",
       "3     37.6450  127.022   45.7160  2.534800      5856.964844       31.7  \n",
       "4     37.5507  127.135   35.0380  0.505500      5859.552246       31.2  \n",
       "...       ...      ...       ...       ...              ...        ...  \n",
       "7747  37.5372  126.891   15.5876  0.155400      4443.313965       28.3  \n",
       "7748  37.5237  126.909   17.2956  0.222300      4438.373535       28.6  \n",
       "7749  37.5237  126.970   19.5844  0.271300      4451.345215       27.8  \n",
       "7750  37.4562  126.826   12.3700  0.098475      4329.520508       17.4  \n",
       "7751  37.6450  127.135  212.3350  5.178230      5992.895996       38.9  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "      <td>0.179382</td>\n",
       "      <td>-0.255999</td>\n",
       "      <td>-1.211640</td>\n",
       "      <td>-0.609973</td>\n",
       "      <td>-0.382551</td>\n",
       "      <td>-0.458084</td>\n",
       "      <td>-0.627756</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.269873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.758450</td>\n",
       "      <td>0.945089</td>\n",
       "      <td>0.050188</td>\n",
       "      <td>-0.339552</td>\n",
       "      <td>0.112082</td>\n",
       "      <td>-0.583062</td>\n",
       "      <td>-0.505076</td>\n",
       "      <td>-0.630414</td>\n",
       "      <td>-0.658679</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.460217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.559805</td>\n",
       "      <td>0.255821</td>\n",
       "      <td>-0.147178</td>\n",
       "      <td>-0.325012</td>\n",
       "      <td>1.350329</td>\n",
       "      <td>-0.831808</td>\n",
       "      <td>-0.411653</td>\n",
       "      <td>-0.559228</td>\n",
       "      <td>-0.518238</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.301597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7747</td>\n",
       "      <td>-2.121901</td>\n",
       "      <td>-0.973302</td>\n",
       "      <td>-1.069982</td>\n",
       "      <td>-0.254168</td>\n",
       "      <td>0.297684</td>\n",
       "      <td>-1.296533</td>\n",
       "      <td>-1.069548</td>\n",
       "      <td>-1.277271</td>\n",
       "      <td>-1.185733</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "      <td>-0.618401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7748</td>\n",
       "      <td>-2.121901</td>\n",
       "      <td>-1.144829</td>\n",
       "      <td>-0.850090</td>\n",
       "      <td>-0.186750</td>\n",
       "      <td>-0.429838</td>\n",
       "      <td>-1.274174</td>\n",
       "      <td>-1.093194</td>\n",
       "      <td>-1.277271</td>\n",
       "      <td>-1.185733</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "      <td>-0.523229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7749</td>\n",
       "      <td>-2.155008</td>\n",
       "      <td>-1.150408</td>\n",
       "      <td>-0.539580</td>\n",
       "      <td>-0.058992</td>\n",
       "      <td>-1.548273</td>\n",
       "      <td>-1.224093</td>\n",
       "      <td>-1.151966</td>\n",
       "      <td>-1.277271</td>\n",
       "      <td>-1.182607</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "      <td>-0.777022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7750</td>\n",
       "      <td>-3.214448</td>\n",
       "      <td>-3.144345</td>\n",
       "      <td>-3.985934</td>\n",
       "      <td>-0.813219</td>\n",
       "      <td>-2.213540</td>\n",
       "      <td>-1.411531</td>\n",
       "      <td>-1.385081</td>\n",
       "      <td>-1.277271</td>\n",
       "      <td>-1.185733</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.164030</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.076326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7751</td>\n",
       "      <td>2.612470</td>\n",
       "      <td>1.328081</td>\n",
       "      <td>3.003060</td>\n",
       "      <td>2.434458</td>\n",
       "      <td>4.441622</td>\n",
       "      <td>2.292088</td>\n",
       "      <td>2.385477</td>\n",
       "      <td>2.671483</td>\n",
       "      <td>2.640931</td>\n",
       "      <td>11.935838</td>\n",
       "      <td>4.172179</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>2.744351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.334097     0.360513         -0.494612 -0.139500  0.221273   \n",
       "1         0.725343     0.304788          0.098990 -0.332391 -0.292172   \n",
       "2         0.626020    -0.417435          0.179382 -0.255999 -1.211640   \n",
       "3         0.758450     0.945089          0.050188 -0.339552  0.112082   \n",
       "4         0.559805     0.255821         -0.147178 -0.325012  1.350329   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7747     -2.121901    -0.973302         -1.069982 -0.254168  0.297684   \n",
       "7748     -2.121901    -1.144829         -0.850090 -0.186750 -0.429838   \n",
       "7749     -2.155008    -1.150408         -0.539580 -0.058992 -1.548273   \n",
       "7750     -3.214448    -3.144345         -3.985934 -0.813219 -2.213540   \n",
       "7751      2.612470     1.328081          3.003060  2.434458  4.441622   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.515767  -0.591155  -0.628249  -0.671715   -0.305211   -0.164030   \n",
       "1     -0.548080  -0.404738  -0.637291  -0.684282   -0.305211   -0.164030   \n",
       "2     -0.609973  -0.382551  -0.458084  -0.627756   -0.305211   -0.164030   \n",
       "3     -0.583062  -0.505076  -0.630414  -0.658679   -0.305211   -0.164030   \n",
       "4     -0.831808  -0.411653  -0.559228  -0.518238   -0.305211   -0.164030   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747  -1.296533  -1.069548  -1.277271  -1.185733   -0.305211   -0.164030   \n",
       "7748  -1.274174  -1.093194  -1.277271  -1.185733   -0.305211   -0.164030   \n",
       "7749  -1.224093  -1.151966  -1.277271  -1.182607   -0.305211   -0.164030   \n",
       "7750  -1.411531  -1.385081  -1.277271  -1.185733   -0.305211   -0.164030   \n",
       "7751   2.292088   2.385477   2.671483   2.640931   11.935838    4.172179   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  -0.364609  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950   0.079528  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534   0.269873  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176   0.460217  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205   0.301597  \n",
       "...        ...       ...       ...       ...              ...        ...  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  -0.618401  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  -0.523229  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  -0.777022  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.076326  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935   2.744351  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "#标准化处理连续型特征 （10分）\n",
    "scaler = StandardScaler().fit(Max)\n",
    "Scaled_Data = pd.DataFrame(scaler.transform(Max))\n",
    "Scaled_Data.columns = Max.columns\n",
    "Scaled_Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.334097</td>\n",
       "      <td>0.360513</td>\n",
       "      <td>-0.494612</td>\n",
       "      <td>-0.139500</td>\n",
       "      <td>0.221273</td>\n",
       "      <td>-0.515767</td>\n",
       "      <td>-0.591155</td>\n",
       "      <td>-0.628249</td>\n",
       "      <td>-0.671715</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.16403</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.725343</td>\n",
       "      <td>0.304788</td>\n",
       "      <td>0.098990</td>\n",
       "      <td>-0.332391</td>\n",
       "      <td>-0.292172</td>\n",
       "      <td>-0.548080</td>\n",
       "      <td>-0.404738</td>\n",
       "      <td>-0.637291</td>\n",
       "      <td>-0.684282</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.16403</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.626020</td>\n",
       "      <td>-0.417435</td>\n",
       "      <td>0.179382</td>\n",
       "      <td>-0.255999</td>\n",
       "      <td>-1.211640</td>\n",
       "      <td>-0.609973</td>\n",
       "      <td>-0.382551</td>\n",
       "      <td>-0.458084</td>\n",
       "      <td>-0.627756</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.16403</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.758450</td>\n",
       "      <td>0.945089</td>\n",
       "      <td>0.050188</td>\n",
       "      <td>-0.339552</td>\n",
       "      <td>0.112082</td>\n",
       "      <td>-0.583062</td>\n",
       "      <td>-0.505076</td>\n",
       "      <td>-0.630414</td>\n",
       "      <td>-0.658679</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.16403</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.559805</td>\n",
       "      <td>0.255821</td>\n",
       "      <td>-0.147178</td>\n",
       "      <td>-0.325012</td>\n",
       "      <td>1.350329</td>\n",
       "      <td>-0.831808</td>\n",
       "      <td>-0.411653</td>\n",
       "      <td>-0.559228</td>\n",
       "      <td>-0.518238</td>\n",
       "      <td>-0.305211</td>\n",
       "      <td>-0.16403</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.334097     0.360513         -0.494612 -0.139500  0.221273  -0.515767   \n",
       "1      0.725343     0.304788          0.098990 -0.332391 -0.292172  -0.548080   \n",
       "2      0.626020    -0.417435          0.179382 -0.255999 -1.211640  -0.609973   \n",
       "3      0.758450     0.945089          0.050188 -0.339552  0.112082  -0.583062   \n",
       "4      0.559805     0.255821         -0.147178 -0.325012  1.350329  -0.831808   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.591155  -0.628249  -0.671715   -0.305211    -0.16403  1.189286   \n",
       "1  -0.404738  -0.637291  -0.684282   -0.305211    -0.16403  1.189286   \n",
       "2  -0.382551  -0.458084  -0.627756   -0.305211    -0.16403  0.653021   \n",
       "3  -0.505076  -0.630414  -0.658679   -0.305211    -0.16403  1.991696   \n",
       "4  -0.411653  -0.559228  -0.518238   -0.305211    -0.16403  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Scaled_Data.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:09.014361Z",
     "start_time": "2020-06-16T01:35:09.005367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.364609\n",
       "1       0.079528\n",
       "2       0.269873\n",
       "3       0.460217\n",
       "4       0.301597\n",
       "          ...   \n",
       "7747   -0.618401\n",
       "7748   -0.523229\n",
       "7749   -0.777022\n",
       "7750   -4.076326\n",
       "7751    2.744351\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Scaled_Data['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "import sklearn.model_selection as ms\n",
    "train_x,test_x,train_y,test_y = ms.train_test_split(X,Y,test_size=0.20,random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False,\n",
       "      random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model = linear_model.Ridge(0,fit_intercept=True,max_iter=1000)\n",
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7370717010956542"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "pred_y = model.predict(test_x)\n",
    "sm.r2_score(test_y,pred_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算MSE与RMSE（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.25306106015364943\n",
      "RMSE 0.5030517469939344\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"MSE\",mean_squared_error(test_y,pred_y))\n",
    "print(\"RMSE\",np.sqrt(mean_squared_error(test_y,pred_y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "source": [
    "'''\n",
    "从上可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13e7e8d50>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13e7f6150>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e7ddfd0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(test_y)), test_y, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(pred_y)), pred_y, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:33.969981Z",
     "start_time": "2020-06-16T01:22:33.602723Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13b7fc590>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y-TEST')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-PREDICTED')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "plt.scatter(test_y, pred_y, marker = 'o',color = 'blue', s = 40 )\n",
    "plt.xlabel(\"Y-TEST\")\n",
    "plt.ylabel(\"Y-PREDICTED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:26.237802Z",
     "start_time": "2020-06-16T00:15:25.595167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13b9b2090>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y-OBSERVED')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-PREDICTED')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x13b7e5dd0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "error = pred_y - test_y\n",
    "plt.scatter(test_y, error, marker = 'o',color = 'blue', s = 40 )\n",
    "plt.xlabel(\"Y-OBSERVED\")\n",
    "plt.ylabel(\"Y-PREDICTED\")\n",
    "plt.hlines(0,min(test_y),max(test_y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图（10分）\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.379205Z",
     "start_time": "2020-06-16T00:15:26.240804Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#相关性\n",
    "cor = np.corrcoef(Max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-171-33a2e6b4c8e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#####================寻找异常值====================######\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mScaled_Data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Next_Tmax'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mbox\u001b[0;34m(self, by, **kwargs)\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0mlike\u001b[0m \u001b[0meach\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mcolored\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m         \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfigs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    716\u001b[0m         \u001b[0mIf\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw\u001b[0m \u001b[0ma\u001b[0m \u001b[0mtable\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m         \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mtransposed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mmeet\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 718\u001b[0;31m         \u001b[0mIf\u001b[0m \u001b[0ma\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mpassed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mto\u001b[0m \u001b[0mdraw\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    719\u001b[0m         \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    720\u001b[0m     \u001b[0myerr\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlike\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_get_plot_backend\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m   1599\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfigs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m             >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n\u001b[1;32m   1603\u001b[0m             ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTYPE_CHECKING\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mType\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m from pandas.plotting._matplotlib.boxplot import (\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mBoxPlot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mboxplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_standard_colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcreate_subplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflatten_axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_typing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAbstractMethodError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decorators\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Label' from 'pandas._typing' (//anaconda3/lib/python3.7/site-packages/pandas/_typing.py)"
     ]
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "Scaled_Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
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       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录（10分）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6175, 7750], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了两行\n",
    "X\n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.22952887192574323\n",
      "RMSE: 0.4790917155678474\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.726012Z",
     "start_time": "2020-06-16T00:15:27.652048Z"
    }
   },
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE: 0.25266554606897074\n",
    "# RMSE: 0.502658478560713  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.5降低到了0.48，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7455182172970509, 0.759486375370554)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388376\n",
      "RMSE: 0.4841852350845529\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
       "      <td>-0.203376</td>\n",
       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "             7          8         9        10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
       "      <td>0.259139</td>\n",
       "      <td>-0.363300</td>\n",
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       "      <td>0.076506</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
       "      <td>0.459231</td>\n",
       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0  -0.228159  0.259139 -0.363300  0.214156 -0.098169  0.389183  0.420652   \n",
       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "           7         8         9        10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535957, 0.7383072663535957)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "import sklearn.ensemble as se\n",
    "X_train, X_test, Y_train, Y_test = ms.train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "\n",
    "\n",
    "model = se.RandomForestRegressor(max_depth=10,n_estimators=1000,min_samples_split=3)\n",
    "params = [{'n_estimators':range(100,500),\n",
    "           'max_depth':range(1,50),\n",
    "           'min_samples_split':[3,4,5,6,7]}\n",
    "                    ]\n",
    "\n",
    "GS = ms.GridSearchCV(model,params,cv=5)\n",
    "GS.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=181,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9150260289679837, 0.8561379054489956)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # 使用交叉验证---随机森林回归器在测试集上的MSE平均得分（5分）\n",
    "\n",
    "s = ms.cross_val_score(GS,X_train,Y_train,cv=5,scoring='f1_weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "训练集上的MSE由原来的0.25降到了0.15，R²由原来的0.75上升到了0.85。\n",
    "'''"
   ]
  }
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